AI-Powered Customer Research for Insurance Companies (2026)

Insurance is one of the hardest industries to research well - and one with the most to gain from doing it. Policyholders only think about their insurer at a few emotionally charged moments (buying, renewing, and claiming), engagement with traditional surveys is low, and the journeys are long and regulated. AI interviews change the economics: with a platform like Koji, an insurer can conduct hundreds of voice or text conversations with policyholders, probe the story behind every claim or cancellation, and get an automatically analyzed report - without a panel agency, a moderator, or a six-week timeline.

This guide covers the highest-value research use cases across the insurance lifecycle and how to run each one.

Why Insurance Research Is Different

Four things make insurance research uniquely challenging:

• Emotionally charged moments. A claim is often filed during a stressful life event. A static survey cannot read tone or follow up on a painful detail; a conversation can.
• Low baseline engagement. Most policyholders ignore email surveys. Response rates are poor, and the people who do respond skew to the extremes.
• Long, multi-touch journeys. Quote, bind, service, claim, renewal - friction at any stage drives churn months later.
• Regulation and sensitivity. Insurance data is sensitive, and health insurance touches PHI, so compliance has to be designed in, not bolted on.

The result: insurers often fly blind on the why behind their NPS and retention numbers. AI interviews close that gap.

High-Value Research Use Cases for Insurers

1. Claims-experience research

The claim is the moment of truth. Run interviews with recent claimants - approved and denied - to learn where the process felt slow, opaque, or unfair. Voice mode captures the emotion; the AI probes "What would have made that easier?" so you get actionable friction, not just a score.

2. Policyholder satisfaction and NPS drivers

Stop guessing why your NPS moved. Pair a scale question (0-10 likelihood to recommend) with adaptive probing so every detractor and promoter explains their rating. See the NPS Survey Guide.

3. Retention and churn research

Interview policyholders who cancelled or switched. Was it price, a bad claim, a competitor offer, or a life change? The AI digs into the real trigger - the kind of insight that a checkbox exit survey never surfaces.

4. Product and coverage concept testing

Before launching a new rider, add-on, or coverage tier, test it. Use single_choice and multiple_choice questions for coverage preferences and a ranking question to prioritize features, all alongside open-ended reactions.

5. Pricing and willingness-to-pay

Understand price sensitivity for new products with conversational pricing research - the AI explores not just what a policyholder would pay but why a price feels fair or excessive.

6. Digital onboarding and quote-flow usability

Where do prospects abandon the online quote? Run task-based interviews on your quote and bind flow to find the drop-off points.

7. Agent and broker channel feedback

For intermediated lines, interview agents and brokers about tooling, commissions, and the support they need to sell more effectively.

How Koji Powers Insurance Research

• Voice or text, on the policyholder's schedule. Participants join via a link 24/7 - no call center, no calendar coordination. Voice mode is ideal for emotional claims stories; text suits quick coverage or pricing checks.
• Adaptive AI follow-ups. Koji probes hesitation and vague answers in real time, the way a skilled researcher would, surfacing the root cause behind a complaint.
• Structured questions for hard numbers. Combine qualitative depth with chartable data: scale for NPS/CSAT, single_choice and multiple_choice for coverage and channel preferences, ranking for feature priorities, and yes_no for claim resolution. See the Structured Questions Guide.
• Methodology built in. Studies run on real frameworks like Customer Discovery and Jobs to be Done, so the AI asks disciplined, non-leading questions.
• Automatic, segmentable reporting. Koji aggregates every interview into a report with themes, verbatim quotes, and distribution charts, and you can segment by policy type, tenure, or claim status.

Compliance and Data Handling

Insurance research must respect strict data rules. Koji supports GDPR-aligned consent and transcript anonymization, and PrimeClub members can run on their own model keys (BYOK) so conversations are never used to train third-party models. For health-insurance research where protected health information may surface, follow the HIPAA-focused guidance and practice data minimization - collect only the personal detail your analysis truly needs. See GDPR-Compliant AI User Research and HIPAA-Compliant AI User Research.

A Simple Way to Start

1. Pick one moment of truth - usually the claims experience or a recent-churn cohort.
2. Create a Koji study and choose Customer Discovery as the methodology.
3. Add 4-6 questions, mixing one open-ended claims story, an NPS scale question, and a coverage-preference choice question.
4. Choose voice for emotional depth, text for speed.
5. Import or share the link with the cohort and let interviews run in parallel.
6. Read the report - themes and NPS drivers are ready as soon as interviews complete.

With new accounts getting 10 free credits, you can field a first claims-experience study before lunch. That is the 10x advantage over commissioning a panel study: insight in days, at a fraction of the cost.

Worked Example: A Claims-Experience Study

Here is what a full study looks like end to end, so you can copy the shape.

Goal: Understand why claims satisfaction dipped last quarter among auto policyholders.

Audience: 30 recent claimants - a mix of approved and denied claims, filed in the last 60 days.

Interview plan (voice mode, ~7 minutes):

1. Open-ended: "Walk me through what happened from the moment you needed to file a claim." (The AI probes for the emotional and practical friction points.)
2. Scale (0-10): "How likely are you to recommend us to a friend?" with anchor probing on the rating.
3. Single choice: "At which stage was the experience most frustrating?" (Filing / Waiting for a decision / Communication / Payout / None)
4. Yes/No: "Did you always know the status of your claim?"
5. Open-ended: "If you could change one thing about the process, what would it be?"

What you get back: Within a day or two, Koji aggregates all 30 interviews into a report. You see the NPS distribution split by approved vs denied, a bar chart of the most frustrating stage, and the recurring themes - say, "unclear status updates" and "slow adjuster callbacks" - each backed by verbatim policyholder quotes. That is a board-ready insight in days, not a six-week panel engagement.

Which Use Case Fits Each Line of Business

Start with the moment that most directly drives churn or complaints for your line, prove the value with one study, then expand into an always-on voice-of-customer program. Because Koji runs interviews in parallel and analyzes them automatically, scaling from one study to a continuous program does not require hiring a research team - the platform absorbs the operational load that traditionally capped how much research an insurer could do.

Related Resources

• Structured Questions in AI Interviews - the six question types for insurance research
• Build a Voice-of-Customer Program - make policyholder listening continuous
• NPS Survey Guide - measure and explain loyalty
• Customer Discovery Interviews at Scale - talk to 100 policyholders in a week
• GDPR-Compliant AI User Research - run compliant studies
• HIPAA-Compliant AI User Research - for health-insurance research